CoConut: Co-classification with output space regularization

In this work we introduce a new approach to co-classification, i.e. the task of jointly classifying multiple, otherwise independent, data samples. The method we present, named CoConut, is based on the idea of adding a regularizer in the label space to encode certain priors on the resulting labelings. A regularizer that encourages labelings that are smooth across the test set, for instance, can be seen as a test-time variant of the cluster assumption, which has been proven useful at training time in semi-supervised learning. A regularizer that introduces a preference for certain class proportions can be regarded as a prior distribution on the class labels. CoConut can build on existing classifiers without making any assumptions on how they were obtained and without the need to re-train them. The use of a regularizer adds a new level of flexibility. It allows the integration of potentially new information at test time, even in other modalities than what the classifiers were trained on. We evaluate our framework on six datasets, reporting a clear performance gain in classification accuracy compared to the standard classification setup that predicts labels for each test sample separately

Leveraging Structure in Activity Recognition: Context and Spatiotemporal Dynamics

Activity recognition is one of the fundamental problems of computer vision. An activity recognition system aims to identify the actions of humans from an image or a video. This problem has been historically approached in isolation, and typically as part of a multi-stage system, where tracking for instance is another part. However, recent work sheds light on how activity recognition is in fact entangled with other fundamental problems in the field. Tracking is one such instance, where the identity of each person is maintained across a video sequence. Scene classification is another example, where scene properties are identified from image data. Affordance reasoning is yet another, where the objects in the scene are assigned labels representing what types of actions can be performed upon them. In this thesis we build a joint formulation for activity recognition, modeling the aforementioned coupled problems as latent variables. Optimizing the objective function for this formulation allows us to recover a more accurate solution to activity recognition and simultaneously solutions to problems like tracking or scene classification. We first introduce a model that jointly solves tracking and activity recognition from videos. Instead of establishing tracks in a preprocessing step, the model solves a joint optimization problem, recovering actions and identities for every person in a video sequence. We then extend this model to include frame-level cues, where activity labels assigned to people in the same scene are inter-compatible through a scene-level label. In the second half of the thesis we look at an alternative formulation of the same problem, based on probabilistic logic. This new model leverages the same cues, temporal and spatial, through soft logic rules. This joint formulation can be efficiently solved, recovering both action labels and tracks. We finally introduce another model that reformulates action recognition in the multi-label setting, where each person can be performing more than one action at the same time. In this setting, a joint formulation can solve for all the likely actions of a person through explicit modeling of action label correlations. Finally, we conclude with a discussion of several challenges and how they can motivate viable future extensions

Performance of Micelle-Clay Filters for Removing Pollutants and Bacteria from Tertiary Treated Wastewater

Filters filled with a micelle-clay complex mixed with sand were employed to investigate their purification capability of tertiary treated wastewater with loose UF-membranes. The UF membrane was hollow fiber with a molecular weight cutoff of 100 kD. The complex was prepared from the organic cation octadecyltrimethylammonium (ODTMA) and the negatively charged clay-mineral, montmorillonite. This complex has a very large surface area, which includes large hydrophobic domains and is positively charged, about half of the cation exchange capacity of the clay. Two sets of filtration experiments were carried out at flow rates of 1.2 and 50 mL/min, which correspond to flow velocities of 3.7 and 153 cm/h, respectively. In the first case, after a passage of 1 L, the turbidity, total suspended solids (TSS), fecal coliforms (FC), and total coliforms (TC) were reduced to zero from 14 NTU, 6 ppm, 350 and 10,000 counts per 100 mL, respectively. In the second case, the numbers of FC and TC were reduced from 50,000/100 mL to zero after the passage of 14 L. The values of COD and BOD were reduced several-fold. The conclusion is that the incorporation of micelle-clay filters in the sewage treatment system with loose tertiary capability is promising and warrants larger scale experiments for optimization of the overall system.This work was supported by a generous grant from United States Agency for International Development (USAID), Middle East Regional Cooperation (MERC) program

Rosemary (Rosmarinus officinalis) plants irrigation with secondary treated effluents using Epuvalisation technology

The secondary treated effluent (STE) of biological treatment process of Al-Quds University wastewater treatment plant was utilized to irrigate rosemary (Rosmarinus officinalis) using an Epuvalisation system. The Epuvalisation technology is a hydroponic treatment system in which the roots of high value crops are used to polish secondary treated wastewater. This technology is considered to be cheap in construction, easy operation, a long lifespan and needs low maintenance requirements. The plant growth parameters (plant height, fresh and dry weight) demonstrated no significant difference between irrigation with STE and fresh water (FW). Further, the plant analysis of rosemary roots, leaves and stems revealed no influence in irrigation with STE as compared to FW. In addition, no accumulation of sodium or chloride ions in the plant tissues was detected showing no effect of both ions in plants. The water quality of both STE and FW indicated a decrease of biological oxygen demand (BOD) and total dissolved solid (TDS). For STE a reduction of 24 and 39%, respectively, was found, whereas for FW the reduction was 29 and 31%, respectively. The combined results suggest that the Epuvalisation technology is a promising technique for growth of high value plants using STE with additional value of further water treatment using rosemary plants that has the ability to tolerate salty water. The results show that the growth parameters and the chemical and physical characteristics of the plant in the secondary treated wastewater channels are similar to those grown in fresh water channels. Furthermore, water characteristics are analyzed during the growing season and show significant reduction in COD, BOD, TDS and TSS. Thus, the Epuvalisation system displays a benefit of polishing the secondary treated effluent to a level that could be discharged to the environment safely with the benefit of growing highly beneficial crop

Learning an efficient model of hand shape variation from depth images

We describe how to learn a compact and efficient model of the surface deformation of human hands. The model is built from a set of noisy depth images of a diverse set of subjects performing different poses with their hands. We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model. The model simultaneously accounts for variation in subject-specific shape and subject-agnostic pose. Specifically, hand shape is parameterized as a linear combination of a mean mesh in a neutral pose with a small number of offset vectors. This mesh is then articulated using standard linear blend skinning (LBS) to generate the con-trol mesh of a subdivision surface. We define an energy that encourages each depth pixel to be explained by our model, and the use of a smooth subdivision surface allows us to op-timize for all parameters jointly from a rough initialization. The efficacy of our method is demonstrated using both syn-thetic and real data, where it is shown that hand shape vari-ation can be represented using only a small number of basis components. We compare with other approaches including PCA and show a substantial improvement in the representa-tional power of our model, while maintaining the efficiency of a linear shape basis. 1